Valeriy Glukhov

Stable Low-rank Tensor Decomposition for Compression of Convolutional Neural Network

Most state-of-the-art deep neural networks are overparameterized and exhibit a high computational cost. A straightforward approach to this problem is to replace convolutional kernels with its low-rank tensor approximations, whereas the Canonical Polyadic tensor Decomposition is one of the most suite…